如何将串行输入拆分为消息

时间：2019-10-07 15:45:54

标签： python python-3.x serial-port pyserial

我在Raspberry Pi上有2个串行端口。当前，代码正在从端口1读取数据并将其写入端口2，反之亦然。我想做的是根据两个指定字符（例如＃或！）将我从两个端口读取的输入拆分为不同的消息（字符组）

此外，如何最终修改当前的“ for”循环，以便可以拆分两个端口的消息，当前代码仅用于从1个端口拆分数据。

我已经尝试过split（），它给出了类型错误。原因可能是串行输入可能是其他类型

org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 64.0 failed 4 times, most recent failure: Lost task 0.3 in stage 64.0 (TID 9047, 10.2.91.129, executor 6): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (array<string>) => array<string>)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.project_doConsume_0$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.NullPointerException
    at $anonfun$_searchGeocode$1.apply(<console>:197)
    at $anonfun$_searchGeocode$1.apply(<console>:196)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
    at _searchGeocode(<console>:196)
    at $anonfun$1.apply(<console>:192)
    at $anonfun$1.apply(<console>:192)
    ... 22 more

Driver stacktrace:
  at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
  at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
  at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
  at scala.Option.foreach(Option.scala:257)
  at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
  at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
  at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
  at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
  at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
  at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
  at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
  at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
  at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
  at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
  at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
  at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
  at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
  at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
  at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
  at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:751)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:710)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:719)
  ... 77 elided
Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (array<string>) => array<string>)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.project_doConsume_0$(Unknown Source)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
  at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
  at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
  at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
  at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
  at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
  at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
  at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
  at org.apache.spark.scheduler.Task.run(Task.scala:123)
  at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
  at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
  at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
  ... 3 more
Caused by: java.lang.NullPointerException
  at $anonfun$_searchGeocode$1.apply(<console>:197)
  at $anonfun$_searchGeocode$1.apply(<console>:196)
  at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
  at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
  at _searchGeocode(<console>:196)
  at $anonfun$1.apply(<console>:192)
  at $anonfun$1.apply(<console>:192)
  ... 22 more

预期结果示例： 如果输入是：

import serial
ser1 = serial.Serial('/dev/ttyUSB0', timeout=2)
ser2 = serial.Serial('/dev/ttyUSB1', timeout=2)
print (ser1)
print (ser2)
ser1_list = []
ser2_list = []

while (True):
    data1 = ser1.readlines()
    data2 = ser2.readlines()
    if data1 or data2:
        ser1_list.extend(data1)
        ser2.writelines(data1)
        byte_split1 = ser1_list.split("1")
        ser2_list.extend(data2)
        ser1.writelines(data2)
        byte_split2 = ser1.split('1')
        for x in byte_split1:
            print(x)
    else:
        break
ser1.close()
ser2.close()

它将输出：

abcde#fghi#jklmnop#

1 个答案:

答案 0 :(得分：0)

您似乎正在尝试在两个位置之间建立聊天之类的功能。请考虑查找如何以规范的方式进行操作：

将其拆分为并行进程，每个并行处理ser1 => ser2，另一个并行处理ser2 => ser1。每个进程都将按自己的方向处理通信。

这使您可以为每个端口写一个 listener ；您的两个过程将是相同的，除了用相反的顺序实例化它们。每个侦听器都收集流量，直到到达分隔符为止。在那一点上，它将写缓冲区内容到该点并移动缓冲区指针。有很多I / O软件包可以为您完成此操作；您只是使用该分隔符“分块”了流。

这应该是足够的指导和参考，供您查找所需的示例。

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